adg_get.RdADG get from pig performance test station csv data
adg_get(
data,
my_break = NULL,
range_offset = 0.5,
threshold = 1,
save_path = NULL
)A data frame or data table containing the nedap or fire pig performance test data to be processed. Columns must include 'visit_time', 'location', 'responder', 'feed_intake'.
Optional, a numeric vector of length 2, indicates target weight range for calculating ADG, default NULL. If not NULL, ADG will be calculated within this range.
Optional, a numeric value, default 0.5. Used to extend the target weight range specified in my_break to avoid border effect. For example, if my_break is c(60,90) and range_offset is 0.5, the actual range for analysis will be 57.5~92.5.
Optional, a numeric value, default 1, used as the threshold to identify outliers in RANSAC regression, usually 0~2.
Optional, a character string specifying where to save the generated growth curve images. If not NULL, images will be saved to this path.
A list containing:
adg_info: A data.table containing ADG statistics
adg_data: A data.table containing processed sample data
nedap_csv_data <- mintyr::nedap
adg_results <- adg_get(data = nedap_csv_data)
#> • There are no duplicate responders in different locations.
#> • The removing of weight < 15kg will not delete responder.
#> • Removing records of missing will delete responders: 1
#> • Deleted responders:
#> c("15964")
#> • Running RANSAC Robust Regression:
#> • RANSAC Robust Regression succeeded!
#> • The outliers detected by Robust model will not delete responder.
#> • All responders' begin_test_weight are less than or equal to 60kg.
#> • Removing end_test_weight <85kg records will delete responders: 1
#> • Deleted responders:
#> c("15967")
#> • Running Simple Linear Regression
#> • Calculate ADG using Simple Linear Regression succeeded!
head(adg_results$adg_info)
#> Key: <responder>
#> responder location start_date_origin min_weight_origin end_date_origin
#> <char> <char> <Date> <num> <Date>
#> 1: 13913 101 2024-02-19 21866.22 2024-05-25
#> 2: 13918 101 2024-02-19 16529.06 2024-05-25
#> 3: 13935 102 2024-02-19 21414.80 2024-05-25
#> 4: 13954 101 2024-02-20 30876.25 2024-05-25
#> 5: 13996 101 2024-02-19 22753.31 2024-05-25
#> 6: 14260 102 2024-02-19 27757.93 2024-05-25
#> max_weight_origin r_squared lm_slope
#> <num> <num> <num>
#> 1: 132895.4 0.9914949 1180.5834
#> 2: 121102.8 0.9917870 1089.6744
#> 3: 138871.4 0.9976035 1233.5471
#> 4: 118602.7 0.9781564 941.2985
#> 5: 142956.6 0.9957625 1281.2277
#> 6: 142547.7 0.9962627 1221.0429